viz
Virgilio Gonzenbach
3/10/2023
library(dplyr)
library(ggplot2)
library(purrr)
library(tibble)
library(tidyr)
library(stringr)Data visualization
In this notebook, the average tensor values extracted across ROIs are plotted before filtering and aggregation of the data. All regions are included.
Abbreviations:
White Matter (WM)
Grey Matter (GM)
Cerebrospinal Fluid (CSF)
Ventricles (VN)
Fractional Asinotropy (FA)
Mean Diffusivity (MD)
Axial Diffusivity (AD)
Radial Diffusivity (RD)
Colors in the plots represent distinct subjects.
seg_df = read.csv('avg_tensor_by_roi.csv', colClasses = c('roi' = 'character')) %>%
mutate(subject = str_extract(sub, "(?<=sub-)\\d{5}"),
site = str_extract(sub, "(?<=sub-\\d{5})\\D+"),
visit = str_extract(sub, "[0-9]{2}$"),
.after = sub)
seg_df[,1] <- NULL
jlf_dict = readxl::read_xlsx('/home/vgonzenb/MSKIDS/data/MUSE_ROI_Dict.xlsx') %>%
filter(ROI_INDEX %in% 1:207)#' Name ROIs according to Segmentation
#'
#' essentially, name_rois enables distinct recoding schemes of the 'roi' column according to the value in the 'segmentation' column.
#' for usage, split or filter a data.frame by unique segmentations and indicate the segmentation type
#' @param df a data.frame corresponding to data from one segmentation type only
#' @param seg_type the segmentation type: 'atropos', 'fast', 'first', 'jlfseg_WMGM', or 'jlfseg_thal'
#'@examples
#'\dontrun{
#' multiseg_df %>% split(seg_df$segmentation) %>% purrr::imap(name_rois) %>% dplyr::bind_rows()
#'}
#'@examples
#'\dontrun{
#' multiseg_df %>% filter(segmentation == seg_type) %>% name_rois(seg_type)
#'}
#' @return a data.frame with a recoded roi column
name_rois <- function(df, seg_type){
roi_names_by_seg_type <- list(atropos = c(`1` = "CSF", `2` = "GM", `3` = "WM"),
fast = c(`1` = "CSF", `2` = "GM", `3` = "WM"),
first = c(`10` = "L. Thalamus",
`11` = "L. Caudate",
`12` = "L. Putamen",
`13` = "L. Pallidum",
`16` = "Brain-Stem/4th Ventricle",
`17` = "L. Hippocampus",
`18` = "L. Amygdala",
`26` = "L. Accumbens-area",
`49` = "R. Thalamus",
`50` = "R. Caudate",
`51` = "R. Putamen",
`52` = "R. Pallidum",
`53` = "R. Hippocampus",
`54` = "R. Amygdala",
`58` = "R. Accumbens-area"),
jlfseg_WMGM = jlf_dict$ROI_NAME |> setNames(jlf_dict$ROI_INDEX),
jlfseg_thal = c(`1` = 'Thalamus'))
roi_names <- roi_names_by_seg_type[[seg_type]]
df %>%
mutate(roi = recode(roi, !!!roi_names))
}
seg_df <- seg_df %>%
split(seg_df$segmentation) %>%
imap(name_rois) %>%
bind_rows()# name tissue types
add_tissue_col <- function(df, seg_type){
tissues_by_seg_type <- list(atropos = setNames(nm = c("CSF", "GM", "WM")),
fast = setNames(nm = c("CSF", "GM", "WM")),
first = setNames(c("Thalamus", "Thalamus"),
c("L.Thalamus", "R.Thalamus")),
jlfseg_WMGM = jlf_dict$TISSUE_SEG |> setNames(jlf_dict$ROI_NAME),
jlfseg_thal = c('Thalamus' = 'Thalamus'))
tissue_type <- tissues_by_seg_type[[seg_type]]
df %>%
mutate(tissue = recode(roi, !!!tissue_type), .after = "roi")
}
seg_df <- seg_df %>%
split(seg_df$segmentation) %>%
imap(add_tissue_col) %>%
bind_rows()# plot each segmentation
plot_seg <- function(mini_df, title=NULL){
title <- paste(title, names(mini_df))
mini_df %>%
ggplot(aes(group=roi, x=roi, color=subject, y=values)) +
geom_boxplot(outlier.shape = NA, alpha = 0.4) +
geom_jitter() +
coord_flip() +
theme(legend.position="none")
}
plot_list <- seg_df %>%
filter(segmentation != 'jlfseg_WMGM') %>%
split(list(.$segmentation, .$tensormap)) %>%
purrr::imap(plot_seg)for (plot_name in names(plot_list)){
header <- plot_name %>%
str_replace("\\.", " ") %>%
str_replace("_", " ") %>%
str_to_upper()
cat("###", header, "\n")
print(plot_list[[plot_name]])
cat('\n\n')
}ATROPOS AD
FAST AD
FIRST AD
JLFSEG THAL AD
ATROPOS FA
FAST FA
FIRST FA
JLFSEG THAL FA
ATROPOS MD
FAST MD
FIRST MD
JLFSEG THAL MD
ATROPOS RD
FAST RD
FIRST RD
JLFSEG THAL RD
plot_list <- seg_df %>%
filter(segmentation == 'jlfseg_WMGM', !roi %in% c('46', '63', '64', '69'), tissue != 'GM') %>%
split(list(.$tissue, .$tensormap)) %>%
purrr::imap(plot_seg)for (plot_name in names(plot_list)){
header <- plot_name %>%
str_replace("\\.", " ") %>%
str_to_upper()
cat("###", "JLF", header, "\n")
print(plot_list[[plot_name]])
cat('\n\n')
}JLF 0 AD
JLF NONE AD
JLF VN AD
JLF WM AD
JLF 0 FA
JLF NONE FA
JLF VN FA
JLF WM FA
JLF 0 MD
JLF NONE MD
JLF VN MD
JLF WM MD
JLF 0 RD
JLF NONE RD
JLF VN RD
JLF WM RD
JLF GM
plot_list <- seg_df %>%
filter(segmentation == 'jlfseg_WMGM', !roi %in% c('46', '63', '64', '69'), tissue == 'GM') %>%
split(list(rep(c(1,2,3,4,5,6), each = 20)[-120], .$tensormap)) %>%
purrr::imap(plot_seg)for (plot_name in names(plot_list)){
header <- plot_name %>%
str_replace("[0-9]+\\.", "") %>%
str_to_upper()
cat("###", "JLF GM", header, "\n")
print(plot_list[[plot_name]])
cat('\n\n')
}JLF GM AD
JLF GM AD
JLF GM AD
JLF GM AD
JLF GM AD
JLF GM AD
JLF GM FA
JLF GM FA
JLF GM FA
JLF GM FA
JLF GM FA
JLF GM FA
JLF GM MD
JLF GM MD
JLF GM MD
JLF GM MD
JLF GM MD
JLF GM MD
JLF GM RD
JLF GM RD
JLF GM RD
JLF GM RD
JLF GM RD
JLF GM RD
Missing Info on Labels
Excluding non brain (label 0), irrelevant segmentations for first. JLF shows three labels 46, 63, 64, 69 that are not in the current data dictionary. This might lead to missing information on
seg_df %>%
mutate(exclude = str_detect(tissue, "^[0-9]+$")) %>%
filter(exclude == TRUE) %>%
filter(tensormap == 'FA') %>% # to reduce the amount of data viewer
group_by(segmentation, tissue) %>%
filter(row_number()==1, roi != 0) %>%
select(-tensormap, -tissue)## Adding missing grouping variables: `tissue`seg_df %>%
mutate(exclude = str_detect(.$tissue, "^[0-9]+$")) %>%
filter(exclude == TRUE) %>%
filter(tissue %in% c('NONE', 'CSF', 'VN'))